Chapter 6 RNA-seq FDA benchmark paper

Title Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

Results

Results

  • Accuracy - as the deviation of RNA-seq pipeline-derived log ratios of gene expression from the corresponding qPCR-based log ratios. A smaller deviation represents higher accuracy. Median normalization exhibited the lowest deviation, or the highest accuracy, compared with all other normalization methods.

  • Precision - as the coefficient of variation (CoV) of gene expression across replicate libraries. Smaller CoV represents higher precision. pipelines with any of Bowtie2 multi-hit, GSNAP un-spliced, or Subread mapping and either count-based or Cufflinks quantification, except for the [Bowtie2 multi-hit + count-based + med.] pipeline, were the best choice for quantifying genes with high precision, or low CoV.

  • Reliability - as the intra-class (i.e., intra-sample in the context of the SEQC-benchmark dataset) correlation (ICC) of gene expression. Larger ICC represents higher reliability. median normalization along with most mapping and quantification algorithms, except for the [Bowtie2 multi-hit + count-based] and [Novoalign + RSEM] pipelines, was the best choice for quantifying genes with high reliability, or high ICC.

Pipeline Strategy * The resulting heatmaps in accrodance to pipeline metrics strategy Heatmaps of Accuracy, Precision, Reliability

Benchmarking datasets Datasets

The tools used in pipelines Tools

Mapping tools used in pipelines Mapping or alignment tools

Quantification tools used in pipelines Quantification tools